Pid parameter adjustment device

ABSTRACT

A PID parameter adjustment device includes a model storage unit ( 1 ) which stores a mathematical model of a controlled system, a PID controller storage unit ( 2 ) which stores a controller algorithm, a constraint condition storage unit ( 3 ) which stores constraint conditions on operation, a simulation computation unit ( 5 ) which executes a simulation to simulate a control system on the basis of the constraint conditions, an ideal control result storage unit ( 6 ) which stores an ideal control response characteristic, an evaluation function computation unit ( 7 ) which computes an evaluation function value representing the proximity between a simulation result and the ideal control response characteristic, and a PID parameter search computation unit ( 8 ) which searches for PID parameters which makes an evaluation function value optimal by executing a simulation while changing the PID parameters.

TECHNICAL FIELD

The present invention relates to a process control technique and, moreparticularly, to a parameter adjustment device which adjusts controlparameters for a controller.

BACKGROUND ART

As a simple method of adjusting PID parameters for a PID controller, anauto-tuning method based on limit cycles has been proposed (see, forexample, patent reference 1). Such a simple method is executed on theassumption that when a controlled system is expressed as a transferfunction, the degree or the like of the function takes a specific value.In this method, PID parameters are roughly adjusted.

In order to make finer adjustment than the above simple method, a methodof determining PID parameters by analyzing process parameters (a processgain, a process time constant, and the like) including the degree of acontrolled system has also been proposed (see, for example, patentreference 2). According to this technique, for example, a modeling stepof approximating a characteristic of a controlled system to a transferfunction expression, and PID parameters are determined by referring tothe modeling results. In this case, traditional adjustment formulas suchas the CHR (Chien, Hrones, Reswick) method and IMC (Internal ModelControl) method are used.

Note that the present applicant has not found any prior art referencesassociated with the present invention other than the prior artreferences specified by the prior art reference information described inthis specification:

patent reference 1: Japanese Patent Laid-Open No. 2000-155603

patent reference 2: Japanese Patent Laid-Open No. 2002-351502

DISCLOSURE OF INVENTION

Problems to be Solved by the Invention

In the conventional PID parameter adjustment method based on modelingresults, if, for example, a controlled system is a second-order lagsystem, there are two time constants. However, the ratio of these timeconstants is, for example, 2:1 or 3:1; this ratio is not uniform. Thatis, modeling results vary. In some cases, therefore, almost optimaladjustment results are obtained by adjustment formulas such as those inthe CHR method and IMC method. In other cases, however, adjustmentresults far from optimal are obtained.

In an actual control site, limit processing based on upper and lowerlimit values is performed for the manipulated variable output from acontroller. That is, in a transition state wherein a controlled variableis changed with a change in a set point as an input to the controller,limit processing based on upper and lower limit values occurs. Since theabove adjustment formulas organized mainly on a theoretical basis arenot premised on the practical constraint operation of the controllersuch as this limit processing, PID parameter adjustment results becomefar from optimal in some cases.

The present invention has been made to solve the above problems, and hasas its object to provide a parameter adjustment device which can copewith the diversity of modeling results and the practical constraintoperation of a controller in adjusting control parameters based onmodeling results.

Means of Solution to the Problem

According to the present invention, there is provided a parameteradjustment device which adjusts a control parameter for a controllerwhich calculates a manipulated variable by performing computation basedon the control parameter, comprising a model storage unit which stores amathematical model of a controlled system in advance, a controllerstorage unit which stores, in advance, a controller algorithm by whichthe controller controls the controlled system, a constraint conditionstorage unit which stores a constraint condition on operation of thecontroller in advance, a simulation computation unit which performs asimulation on the basis of the constraint condition to simulate acontrol response of a control system including the controlled systemrepresented by the mathematical model and a controller represented bythe controller algorithm, an ideal control result storage unit whichstores an ideal control response characteristic of the control system inadvance, an evaluation function computation unit which computes anevaluation function value representing a proximity between a result ofthe simulation and the ideal control response characteristic, and aparameter search computation unit which causes the simulationcomputation unit to execute the simulation while sequentially changingthe control parameter for the controller algorithm, and uses, as aparameter adjustment result, a control parameter which makes theevaluation function value become an optimal value.

In addition, according to the present invention, there is provided aparameter adjustment device comprising a model storage unit which storesa mathematical model of a controlled system in advance, a controllerstorage unit which stores, in advance, a controller algorithm by whichthe controller controls the controlled system, a constraint conditionstorage unit which stores a constraint condition on operation of thecontroller in advance, an ideal control result storage unit which storesan ideal control response characteristic associated with a controlsystem including a controlled system represented by the mathematicalmodel and a controller represented by the controller algorithm, a firstsimulation computation unit which performs a first simulation tosimulate a transition state of the controlled system by applying anupper limit manipulated variable or a lower limit manipulated variabledefined by the constraint condition to the controlled system for amanipulated variable maintenance time, an ideal response result storageunit which stores an ideal response result as an ideal result of thefirst simulation in advance, a first evaluation function computationunit which computes a first evaluation function value representing aproximity between a result of the first simulation and the idealresponse result, a manipulated variable maintenance time searchcomputation unit which extracts a manipulated variable maintenance timewhich makes the first evaluation function value become an optimal valueby causing the first simulation computation unit to execute the firstsimulation while sequentially changing the manipulated variablemaintenance time, an ideal response waveform registration processingunit which registers a result of the first simulation corresponding tothe extracted manipulated variable maintenance time as the ideal controlresponse characteristic in the ideal control result storage unit, asecond simulation computation unit which performs a second simulation tosimulate a control response of the control system on the basis of theconstraint condition, a second evaluation function computation unitwhich computes a second evaluation function value representing aproximity between a result of the second simulation and the idealcontrol response characteristic registered in the ideal control resultstorage unit, and a parameter search computation unit which causes thesecond simulation computation unit to execute the second simulationwhile sequentially changing the control parameter for the controlleralgorithm, and uses, as a parameter adjustment result, a controlparameter which makes the second evaluation function value become anoptimal value.

An example of the arrangement of the parameter adjustment device of thepresent invention comprises an auto-tuning computation unit whichexecutes auto-tuning simulation processing of calculating an estimatedvalue of the control parameter from a response of the controlled systemby applying a manipulated variable with a predetermined amplitude to thecontrolled system, and a parameter search range setting unit whichdetermines a search range of the control parameter on the basis of theestimated value of the control parameter and sets the search range inthe parameter search computation unit.

EFFECTS OF THE INVENTION

According to the present invention, a control system is created on theadjustment device by combining a controller algorithm and a modelingresult (mathematical model) of a controlled system, the differencebetween a simulation result on the controlled system and an idealcontrol response characteristic is provided as an evaluation functionvalue, and the control system is repeatedly simulated so as to make anevaluation function value approach an optimal value, thereby searchingfor optimal control parameters. This makes it possible to accuratelyreflect the modeling result on the controlled system in controlparameter adjustment and cope with the diversity of modeling results. Inaddition, since simulations are performed on the basis of constraintconditions on the operation of the controller, the practical constraintoperation of the controller can be accurately reflected in controlparameter adjustment, thereby coping with the practical constraintoperation.

In addition, the first simulation is performed to simulate thetransition state of a controlled system by applying the upper limitmanipulated variable or lower limit manipulated variable defined byconstraint conditions to the controlled system for the manipulatedvariable maintenance time, and the difference between the firstsimulation result and the ideal response result is provided as the firstevaluation function value. An ideal control response characteristic isobtained by repeating the first simulation so as to make the firstevaluation function value approach an optimal value. This ideal controlresponse characteristic is then registered in the ideal control resultstorage unit, thus searching for the above control parameters. Thismakes it possible to realize optimal control parameter adjustmentwithout requiring the user to have expertise about control.

Furthermore, the limit cycle auto-tuning method of generating limitcycles with a predetermined manipulated variable width and adjustingcontrol parameters is executed by a simulation to calculate estimatedvalues of control parameters. Search ranges are narrowed down on thebasis of the calculated estimated values of the control parameters, andthen a search for control parameters is performed. This makes itpossible to shorten the time required to search for optimal controlparameters.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram showing the arrangement of a PID parameteradjustment device according to the first embodiment of the presentinvention;

FIG. 2 is a flowchart showing the operation of the PID parameteradjustment device in FIG. 1;

FIG. 3 is a timing chart schematically showing an ideal control responsecharacteristic in the first embodiment of the present invention;

FIG. 4 is a block diagram of a control system in the first embodiment ofthe present invention;

FIG. 5 is a block diagram showing the arrangement of a PID parameteradjustment device according to the second embodiment of the presentinvention;

FIG. 6 is a flowchart showing the operation of the PID parameteradjustment device in FIG. 5;

FIGS. 7A to 7F are timing charts for explaining ideal transitionresponse trajectory determination processing in a manipulated variablemaintenance time search computation unit in the second embodiment of thepresent invention;

FIG. 8 is a block diagram showing the arrangement of a PID parameteradjustment device according to the third embodiment of the presentinvention;

FIG. 9 is a flowchart showing the operation of the PID parameteradjustment device in FIG. 8;

FIG. 10 is a flowchart showing the details of deviation extreme valuedetection processing and switching elapsed time detection processing inan auto-tuning computation unit in the third embodiment of the presentinvention; and

FIG. 11 shows timing charts for explaining deviation extreme valuedetection processing and switching elapsed time detection processing inthe auto-tuning computation unit in the third embodiment of the presentinvention.

BEST MODE FOR CARRYING OUT THE INVENTION First Embodiment

An embodiment of the present invention will be described in detail belowwith reference to the accompanying drawings. FIG. 1 is a block diagramshowing the arrangement of a PID parameter adjustment device accordingto the first embodiment of the present invention. FIG. 2 is a flowchartshowing the operation of the PID parameter adjustment device in FIG. 1.

The PID parameter adjustment device of this embodiment comprises a modelstorage unit 1 which stores a mathematical model of a controlled systemin advance, a PID controller storage unit 2 which stores a PIDcontroller algorithm in advance, a constraint condition storage unit 3which stores constraint conditions on the operation of the controller inadvance, a simulation specification storage unit 4 which stores, inadvance, the specifications of a simulation of the control response of acontrol system comprising the controlled system represented by themathematical model and the controller represented by the controlleralgorithm, a simulation computation unit 5 which executes a simulationto simulate the control system on the basis of the constraint conditionson the operation of the controller, an ideal control result storage unit6 which stores the ideal control response characteristic of the controlsystem in advance, an evaluation function computation unit 7 whichcomputes an evaluation function value representing the proximity betweena simulation result and the ideal control response characteristic, and aPID parameter search computation unit 8 which causes the simulationcomputation unit 5 to execute simulations while sequentially changingthe control parameters (PID parameters in this embodiment) of thecontroller algorithm, and uses, as a parameter adjustment result, a PIDparameter by which an optimal evaluation function value is obtained.

The operation of the PID parameter adjustment device according to thisembodiment will be described below. The operation of each constituentelement will be described first, and the flow of overall processing willbe described next with reference to FIG. 2.

Assuming that a controlled system has time lag and dead time factors, atransfer function Gp can be expressed as follows:Gp=Kpexp(−Lps)/{(1+T1s)(1+T2s)(1+T3s)}  (1)where Kp is a process gain, Lp is a process dead time, T1, T2, and T3are process time constants, and s is a Laplace operator.

The mathematical model represented by equation (1) is registered inadvance in the model storage unit 1 by the user of the PID parameteradjustment device. In addition, the process gain Kp, process dead timeLp, and process time constants T1, T2, and T3 which are obtained inadvance by a known modeling technique are registered in the modelstorage unit 1 in advance. According to equation (1), a controlledsystem with up to a third-order lag can be expressed.

The user registers, in the PID controller storage unit 2 in advance, aPID controller algorithm represented by a transfer function like thatgiven by the following equation, i.e., a program for making thesimulation computation unit 5 implement a PID controller:MV=(100/Pb){1+(1/Tis)+Tds}(SP−PV)  (2)where Pb is a proportional band, Ti is an integral time, Td is aderivative time, MV is a manipulated variable, SP is a set point, and PVis a controlled variable. The proportional band Pb, integral time Ti,and derivative time Td are determined by the PID parameter searchcomputation unit 8. In addition, the manipulated variable MV, set pointSP, and controlled variable PV dynamically change as a simulation isexecuted by the simulation computation unit 5.

The user registers the following constraint conditional expressions inthe constraint condition storage unit 3 in advance:if MV>MVH then MV=MVH  (3)if MV<MVL then MV=MVL  (4)where MVH is the upper limit value of the manipulated variable MV outputto the controlled system, and MVL is the lower limit value of themanipulated variable MV. Expression (3) indicates a case wherein if themanipulated variable MV computed by the simulation computation unit 5 islarger than the manipulated variable upper limit value MVH, upper limitprocessing is performed to set MV=MVH, i.e., to set the manipulatedvariable upper limit value MVH to the manipulated variable MV.Expression (4) indicates a case wherein if the computed manipulatedvariable MV is smaller than the manipulated variable lower limit valueMVL, lower limit processing is performed to set MV=MVL, i.e., to set themanipulated variable lower limit value MVL to the manipulated variableMV.

The user registers the simulation specifications represented by thefollowing expressions in the simulation specification storage unit 4 inadvance:if T<Tstep then SP=SP1 else SP=SP2  (5)if T=Tsim then [terminate simulation]  (6)where SP1 is a set point before a step response, SP2 is a set pointafter the step response, T is the elapsed time of a simulation, Tstep isthe time from the start time (T=0) of the simulation to the time of astep change in the set point SP, and Tsim is the total simulation time.

Expression (5) indicates that the set point SP is set to SP1 before theelapsed T from the simulation start time reaches Tstep, and the setpoint SP is set to SP2 when the elapsed time T reaches Tstep. Expression(6) indicates that when the elapsed time T reaches Tsim, the simulationis terminated.

A control response characteristic ideal for the user, i.e., an idealcontrolled variable PV_data_j in elapsed time T=Tj (Tj≦Tsim where j isan integer from 1 to n), is registered in the ideal control resultstorage unit 6 in advance. In order to determine an ideal controlresponse characteristic, at least one specific value of the controlledvariable PV_data_j (n≧1) is required. FIG. 3 schematically shows theideal control response characteristic.

The simulation computation unit 5 performs a simulation to simulate thecontrol response of a virtual control system comprising the controlledsystem represented by the mathematical model in the model storage unit 1and the PID controller represented by the controller algorithm in thePID controller storage unit 2 on the basis of the constraint conditionalexpressions in the constraint condition storage unit 3 and thesimulation specifications in the simulation specification storage unit4. FIG. 4 is a block diagram showing the control system in this case.

The simulation computation unit 5 executes initialization processing andsimulation processing. In the initialization processing, the simulationcomputation unit 5 sets the initial value of the controlled variable PVat the simulation start time to SP1, the initial value of the set pointSP to SP1, the initial value of the manipulated variable MV to SP1/Kp,and the elapsed time T of a simulation to 0. The simulation processingwill be described later.

The evaluation function computation unit 7 executes evaluation functionprocessing of obtaining an evaluation function value F representing theproximity between a simulation result and the ideal control responsecharacteristic as follows. $\begin{matrix}{F = {\sum\limits_{j = 1}^{n}\left( {{PV\_ Tj} - {{PV\_ data}{\_ j}}} \right)^{2}}} & (7)\end{matrix}$where PV_Tj is the controlled variable computed by simulation processingat elapsed time T=Tj, and n is the data count of the ideal controlledvariable PV_data_j, as described above. When the calculation result ofequation (7) becomes 0 or a minimum value (positive value) near 0, theevaluation function value F is an optimal value. In this case, thesimulation result becomes closest to the ideal control responsecharacteristic.

The PID parameter search computation unit 8 executes PID parametersearch processing comprising PID parameter creation processing,evaluation function value comparison processing, and PID parameterdetermination processing.

In the PID parameter creation processing, the PID parameter searchcomputation unit 8 sequentially creates, one by one, all values whichPID parameters can take, i.e., all combinations of the proportionalbands Pb, integral times Ti, and derivative times Td. Letting Pb_max bethe proportional band search upper limit value determined in advance,Ti_max be the integral time search upper limit value determined inadvance, and Td_max be the derivative time search upper limit valuedetermined in advance, the proportional band Pb is created within therange of 0<Pb<Pb_max with a resolution of dPb, the integral time Ti iscreated within the range of 0<Ti<Ti_max with a resolution of dTi, andthe derivative time Td is created within the range of 0<Td<Td_max with aresolution of dTd.

In the evaluation function value comparison processing, the PIDparameter search computation unit 8 compares evaluation function valuesF calculated with respect to all the combinations of the proportionalbands Pb, integral times Ti, and derivative times Td with each other,and extracts a combination of the proportional band Pb, integral timeTi, and derivative time Td which provides a minimum evaluation functionvalue F_min (F_min≧0) of all the evaluation function values F.

In the PID parameter determination processing, the PID parameter searchcomputation unit 8 uses, as a parameter adjustment result, thecombination of the proportional band Pb, integral time Ti, andderivative time Td which is extracted by the evaluation function valuecomparison processing.

Note that the above PID parameter search processing is a technique ofcausing the simulation computation unit 5, which creates, one by one,all the values which PID parameters can take, to execute simulationprocessing and comparing all the calculated evaluation function values Fwith each other to search for the optimal PID parameters. This techniqueis, however, a technique based on no consideration of search efficiency,and is only an example. A generally known simplex method or the like maybe used as an efficient method of making the simulation computation unit5 perform searching operation while sequentially changing PID parametersso as to make the evaluation function value F approach an optimal value.

The flow of processing in the PID parameter adjustment device in FIG. 1will be described next with reference to FIG. 2. The PID parameteradjustment device starts the processing in FIG. 2 in accordance with,for example, a request from the user. First of all, the PID parametersearch computation unit 8 performs PID parameter creation processing(step 101 in FIG. 2). The simulation computation unit 5 performsinitialization processing (step 102).

Subsequently, the simulation computation unit 5 performs simulationprocessing at elapsed time T=0 (step 103). In this simulation processingat elapsed time T=0, the simulation computation unit 5 performs upperlimit processing or lower limit processing for the manipulated variableMV determined in the initialization processing on the basis of the aboveconstraint conditional expressions as needed. The simulation computationunit 5 then computes the controlled variable PV according to thefollowing equation based on the mathematical model in the model storageunit 1 and stores the controlled variable PV as a simulation result incorrespondence with elapsed time T=0.PV=[Kpexp(−Lps)/{(1+T1s)(1+T2s)(1+T3s)}]MV  (8)

In general, after the execution of the simulation processing, theevaluation function computation unit 7 executes evaluation functionprocessing. However, since no ideal control response characteristic isdetermined at elapsed time T=0, evaluation function processing is notexecuted.

After the execution of the simulation processing, the simulationcomputation unit 5 determines whether T≧Tsim holds (step 105). IfT≧Tsim, i.e., the elapsed time T has reached Tsim, it is determined inaccordance with the above simulation specifications that the simulationprocessing is complete, and the flow advances to step 106. If theelapsed time T has not reached Tsim, it is determined that thesimulation processing is not complete, and the flow returns to step 103.

The simulation computation unit 5 then performs simulation processing atelapsed time T>0 (step 103). In this simulation processing at elapsedtime T>0, the simulation computation unit 5 determines whether T≧Tstepholds. If the elapsed time T has not reached Tstep, the simulationcomputation unit 5 keeps setting the set point SP to SP1. If the elapsedtime T has reached Tstep, the simulation computation unit 5 changes theset point SP to SP2. Subsequently, the simulation computation unit 5computes the manipulated variable MV by using the controlled variable PVcomputed in the immediately preceding simulation processing, the currentset point SP, and the PID parameters created in step 101 according tothe PID controller algorithm represented by equation (2). Afterperforming upper limit processing or lower limit processing, as needed,based on the above constraint conditional expressions for the computedmanipulated variable MV, the simulation computation unit 5 computes thecontrolled variable PV according to equation (8), and stores thecontrolled variable PV as a simulation result in correspondence with theelapsed time T.

If elapsed time T>0 is an elapsed time Tj during which the idealcontrolled variable PV_data_j is registered in the ideal control resultstorage unit 6 (T=Tj), since the controlled variable computed by thesimulation computation unit 5 is PV_Tj, the evaluation functioncomputation unit 7 calculates the evaluation function value F from thecontrolled variable PV_Tj and the controlled variable PV_data_jaccording to equation (7) (step 104).

The simulation computation unit 5 and the evaluation functioncomputation unit 7 repeatedly execute the above simulation processingand evaluation function processing at elapsed time T>0 in predeterminedcycles until T≧Tsim holds in step 105.

At some midway point at which elapsed time T≧Tn does not hold, sincethere are still some ideal controlled variables which have not beencompared with the simulation result, the evaluation function processingat this midway point is processing of calculating evaluation functionvalues F as interim values like(PV_T1−PV_data_(—)1)²+(PV_T2−PV_data_(—)2)²+(PV_T3−PV_data_(—)3)² . . ..

If it is determined in step 105 that T≧Tsim holds, the simulationcomputation unit 5 determines that the simulation processing iscomplete, and determines whether the processing in steps 101 to 105 iscomplete for all the combinations of the PID parameters (step 106). Ifthe processing in steps 101 to 105 for all the combinations of the PIDparameters is complete, the flow advances to step 107. If the processingis not complete, the flow returns to step 101 to cause the PID parametersearch computation unit 8 to create a new combination of theproportional band Pb, integral time Ti, and derivative time Td. In thismanner, the processing in steps 101 to 105 is executed for each of allthe combinations of the proportional bands Pb, integral times Ti, andderivative times Td.

If the processing in steps 101 to 105 for all the combinations of thePID parameters is complete, the PID parameter search computation unit 8executes evaluation function value comparison processing, and extracts acombination of the proportional band Pb, integral time Ti, andderivative time Td which provides the minimum evaluation function valueF_min (step 107). The PID parameter search computation unit 8 then usesthe extracted combination of the proportional band Pb, integral time Ti,and derivative time Td as a parameter adjustment result (step 108).

With the above operation, the processing by the PID parameter adjustmentdevice is terminated.

According to this embodiment, a virtual control system is created on theadjustment device by combining a PID controller algorithm and a modelingresult (mathematical model) of a controlled system, the differencebetween a simulation result on the control system and an ideal controlresponse characteristic is provided as an evaluation function value, andthe control system is repeatedly simulated so as to make an evaluationfunction value approach an optimal value, thereby searching for optimalPID parameters. This makes it possible to accurately reflect themodeling result on the controlled system in PID parameter adjustment andcope with the diversity of modeling results. In addition, sincesimulations are performed on the basis of constraint conditions on theoperation of the controller, the practical constraint operation of thecontroller can be accurately reflected in PID parameter adjustment,thereby coping with the practical constraint operation.

Second Embodiment

The second embodiment of the present invention will be described next.FIG. 5 is a block diagram showing the arrangement of a PID parameteradjustment device according to the second embodiment of the presentinvention. FIG. 6 is a flowchart showing the operation of the PIDparameter adjustment device in FIG. 5.

The PID parameter adjustment device of this embodiment comprises aresponse condition storage unit 14 which stores response conditionvariables for the first simulation of a transition state of a controlledsystem in advance, a first simulation specification storage unit 15which stores the specifications of the first simulation, an idealresponse result storage unit 16 which stores an ideal response result asthe ideal result of the first simulation in advance, a first simulationcomputation unit 17 which performs the first simulation by providing anupper limit manipulated variable or a lower limit manipulated variableto the controlled system for a manipulated variable maintenance time, afirst evaluation function computation unit 18 which computes the firstevaluation function value representing the proximity between the firstsimulation result and the ideal response result, a manipulated variablemaintenance time search computation unit 19 which extracts a manipulatedvariable maintenance time as the optimal value of the first evaluationfunction value by causing the first simulation computation unit 17 toexecute the first simulation while sequentially changing the manipulatedvariable maintenance time, an ideal response waveform registrationprocessing unit 20 which registers the first simulation resultcorresponding to the extracted manipulated variable maintenance time asan ideal control response characteristic in an ideal control resultstorage unit 26, a model storage unit 21, a PID controller storage unit22, a constraint condition storage unit 23, a second simulationspecification storage unit 24, a second simulation computation unit 25,the ideal control result storage unit 26, a second evaluation functioncomputation unit 27, and a PID parameter search computation unit 28.

Since the operations of the model storage unit 21, PID controllerstorage unit 22, constraint condition storage unit 23, second simulationspecification storage unit 24, second simulation computation unit 25,second evaluation function computation unit 27, and PID parameter searchcomputation unit 28 are the same as those of the model storage unit 1,PID controller storage unit 2, constraint condition storage unit 3,simulation specification storage unit 4, simulation computation unit 5,evaluation function computation unit 7, and PID parameter searchcomputation unit 8 in the first embodiment, a description thereof willbe omitted.

Although the operation of the ideal control result storage unit 26 isthe same as that of the ideal control result storage unit 6 in the firstembodiment, the ideal control response characteristic stored in theideal control result storage unit 26 is registered not by the user butby the ideal response waveform registration processing unit 20, as willbe described later.

Manipulated variables to be provided to the controlled system during thefirst simulation are registered as response condition variables in theresponse condition storage unit 14 in advance. Note that since theseresponse condition variables are the same as a manipulated variableupper limit value MVH and a manipulated variable lower limit value MVLwritten in constraint conditional expressions (3) and (4) in the firstembodiment, response condition variables may be acquired from theconstraint conditions without using the response condition storage unit14.

The specifications of the first simulation are registered in the firstsimulation specification storage unit 15 in advance by the user. Thespecifications of the first simulation are the same as those of thesimulation processing described with reference to expressions (5) and(6) in the first embodiment.

An overshoot amount OS is registered in advance as an ideal responseresult which is an ideal result of the first simulation in the idealresponse result storage unit 16 by the user.

The first simulation computation unit 17 executes the firstinitialization processing and the first simulation processing on thebasis of the response condition variables in the response conditionstorage unit 14 and the simulation specifications in the simulationspecification storage unit 16.

In the first initialization processing, the simulation computation unit17 sets the initial value of a controlled variable PV at the simulationstart time to SP1, the initial value of a set point SP to SP1, theinitial value of a manipulated variable MV to SP1/Kp, and an elapsedtime T of the simulation to 0.

In the first simulation processing, the simulation computation unit 17performs the following processing while performing the processingrepresented by expression (5) on the basis of the simulationspecifications in the simulation specification storage unit 16:if T<Tstep then MV=SP1/Kp  (9)if Tstep≦T≦Tstep+dTmv then MV=MV _(—) dTmv  (10)if T>Tstep+dTmv then MV=SP2/Kp  (11)In expression (10), dTmv is a manipulated variable maintenance time asthe time during which manipulated variable MV=MV_dTmv is maintained.

Expression (9) indicates that the manipulated variable MV is SP1/Kp atthe time when the elapsed time T from the simulation start time has notreached Tstep.

Expression (10) indicates that the manipulated variable MV is set toMV_dTmv when the elapsed time T is equal to or more than Tstep and equalto or less than Tstep+dTmv. If Kp>0 and SP1<SP2 or Kp<0 and SP1>SP2,MV_dTmv=MVH is set by using the manipulated variable upper limit valueMVH of a response condition variable stored in the response conditionstorage unit 14. If Kp>0 and SP1>SP2 or Kp<0 and SP1<SP2, MV_dTmv=MVL isset by using the manipulated variable lower limit value MVL of aresponse condition variable.

Expression (11) indicates that when the elapsed time T exceedsTstep+dTmv, the manipulated variable MV is set to SP2/Kp.

The first evaluation function computation unit 18 executes the firstevaluation function processing of obtaining a first evaluation functionvalue G representing the proximity between the first simulation resultand the ideal response result according to the following equation:G=(OS _(—) sim−OS)²  (12)where OS_sim is the amount of overshoot that occurs during the firstsimulation. When the calculation result by equation (12) is a minimumvalue (positive value) near 0, the first evaluation function value G isan optimal value. In this case, the first simulation result is nearestto the ideal response result. In the case of the evaluation functionrepresented by equation (12), simulating a nearly accurate continuoussystem makes it possible to execute a simulation sufficiently close toG=0, i.e., OS_sim=OS.

The manipulated variable maintenance time search computation unit 19executes manipulated variable maintenance time search processingcomprising manipulated variable maintenance time creation processing andfirst evaluate function value comparison processing.

In the manipulated variable maintenance time creation processing, themanipulated variable maintenance time search computation unit 19sequentially creates, one by one, all values which the manipulatedvariable maintenance time dTmv can take. Letting dTmv_max be apredetermined manipulated variable maintenance time search upper limitvalue, a range which the manipulated variable maintenance time dTmv cantake is created within the range of 0<dTmv<dTmv_max with a precisioncorresponding to a resolution Dmv.

In the first evaluation function value comparison processing, themanipulated variable maintenance time search computation unit 19compares evaluation function values G calculated with respect to all thevalues which the manipulated variable maintenance time dTmv can take,and extracts the manipulated variable maintenance time dTmv thatprovides a minimum evaluation function value G_min (G_min>0) of all theevaluation function values G.

Note that the above manipulated variable maintenance time searchprocessing is a technique of creating, one by one, all the values whichthe manipulated variable maintenance time dTmv can take, causing thefirst simulation computation unit 17 to execute the first simulationprocessing, and comparing all the calculated evaluation function valuesG, thereby searching for an optimal manipulated variable maintenancetime dTmv. This technique is, however, a technique based on noconsideration of search efficiency, and is only an example.

The ideal response waveform registration processing unit 20 registersthe first simulation results (the elapsed time T and the controlledvariable PV) corresponding to the manipulated variable maintenance timedTmv extracted by the manipulated variable maintenance time searchcomputation unit 19 as an ideal control response characteristic in theideal control result storage unit 26.

After the ideal control response characteristic is registered in theideal control result storage unit 26, the model storage unit 21, PIDcontroller storage unit 22, constraint condition storage unit 23, secondsimulation specification storage unit 24, second simulation computationunit 25, second evaluation function computation unit 27, and PIDparameter search computation unit 28 execute the processing described inthe first embodiment.

The flow of processing in the PID parameter adjustment device in FIG. 5will be described next with reference to FIG. 6. The PID parameteradjustment device starts the processing in FIG. 6 in accordance with,for example, a request from the user. First of all, the manipulatedvariable maintenance time search computation unit 19 performsmanipulated variable maintenance time creation processing (step 201 inFIG. 6), and the first simulation computation unit 17 performs the firstinitialization processing (step 202).

Subsequently, the first simulation computation unit 17 performs thefirst simulation processing at elapsed time T=0 (step 203). In the firstsimulation processing at elapsed time T=0, the simulation computationunit 17 computes the controlled variable PV by substituting manipulatedvariable MV=SP1/Kp determined in the first initialization processinginto equation (8), and stores the controlled variable PV as the firstsimulation result in correspondence with elapsed time T=0.

After the execution of the first simulation processing, the firstevaluation function computation unit 18 executes first evaluationfunction processing. At a time point when no overshoot has occurred inthe controlled variable PV, however, an evaluation function value G iscalculated with overshoot amount OS_sim=0 (step 204).

After the execution of the first simulation processing, the firstsimulation computation unit 17 determines whether the elapsed time T hasreached Tsim (step 205). If the elapsed time T has reached Tsim, it isdetermined in accordance with the simulation specifications in the firstsimulation specification storage unit 15 that the first simulationprocessing is complete, and the flow advances to step 206. If theelapsed time T has not reached Tsim, it is determined that the firstsimulation processing is not complete, and the flow returns to step 203.

The first simulation computation unit 17 performs the first simulationprocessing at elapsed time T>0 (step 203). In the first simulationprocessing at elapsed time T>0, the simulation computation unit 17determines whether T≧Tstep holds. If the elapsed time T has not reachedTstep, the simulation computation unit 17 keeps setting the set point SPto SP1. If the elapsed time T has reached Tstep, the simulationcomputation unit 17 changes the set point SP to SP2. Subsequently, thesimulation computation unit 17 determines the manipulated variable MV byusing one of expressions (9), (10), and (11) in accordance with theelapsed time T, computes the controlled variable PV according toequation (8), and stores the controlled variable PV as the firstsimulation result in correspondence with the elapsed time T.

If Kp>0 and SP1<SP2 or Kp<0 and SP1<SP2, the first evaluation functioncomputation unit 18 determines that overshoot has occurred in thecontrolled variable PV, at a time point at which PV−SP>0 holds. If Kp<0and SP1>SP2 or Kp>0 and SP1>SP2, the first evaluation functioncomputation unit 18 determines that overshoot has occurred, at a timepoint at which PV−SP<0 holds. The first evaluation function computationunit 18 then calculates the evaluation function value G from anovershoot amount OS_sim that has occurred and an ideal overshoot amountOS according to equation (12) (step 204). The overshoot amount OS_simcan be obtained by PV−SP.

The first simulation computation unit 17 and the first evaluationfunction computation unit 18 repeatedly execute the above firstsimulation processing and first evaluation function processing atelapsed time T>0 in predetermined cycles until T≧Tsim holds in step 205.

Subsequently, if it is determined in step 205 that T≧Tsim holds, thefirst simulation computation unit 17 determines that the firstsimulation processing is complete, and determines whether the processingin steps 201 to 205 is complete for all values which the manipulatedvariable maintenance time dTmv can take (step 206). If the processing insteps 201 to 205 is complete for all the values which the manipulatedvariable maintenance time dTmv can take, the flow advances to step 207.If the processing is not complete, the flow returns to step 201 to causethe manipulated variable maintenance time search computation unit 19 tocreate a new value of the manipulated variable maintenance time dTmv. Inthis manner, the processing in steps 201 to 205 is executed for all thevalues which the manipulated variable maintenance time dTmv can take.

When the processing in steps 201 to 205 is complete for all the valueswhich the manipulated variable maintenance time dTmv can take, themanipulated variable maintenance time search computation unit 19executes the first evaluation function value comparison processing toextract the manipulated variable maintenance time dTmv which provides aminimum evaluation function value G_min (step 207). The ideal responsewaveform registration processing unit 20 registers the first simulationresult corresponding to the extracted manipulated variable maintenancetime dTmv as an ideal control response characteristic in the idealcontrol result storage unit 26 (step 208).

In the first simulation, the controlled variable PV at elapsed time T(0<T≦Tsim) is computed, and the elapsed time T and the controlledvariable PV are stored as simulation results in the simulationcomputation unit 17. Of the first simulation results stored in thesimulation computation unit 17, therefore, a result corresponding to theextracted manipulated variable maintenance time dTmv may be registeredin the ideal control result storage unit 26.

FIGS. 7A to 7F are views for explaining ideal transition responsetrajectory determination processing by the manipulated variablemaintenance time search computation unit 19. FIG. 7B shows the result ofthe first simulation in which the manipulated variable MV with awaveform like that shown in FIG. 7A is applied to a controlled system.In the case shown in FIG. 7B, no overshoot occurs, and the controlledvariable PV slowly approaches the set point SP2. That is, therequirement for the quick response characteristic of control is notsatisfied. This response characteristic is therefore unsuitable as anideal control response characteristic.

FIG. 7D shows the result of the first simulation in which themanipulated variable MV with a waveform like that shown in FIG. 7C isapplied to the controlled system. In the case shown in FIG. 7D, largeovershoot has occurred to result in excessive control response. That is,this response characteristic is unsuitable for an ideal control responsecharacteristic.

FIG. 7F shows the result of the first simulation in which themanipulated variable MV with a waveform like that shown in FIG. 7E isapplied to the controlled system. In the case shown in FIG. 7F,overshoot which is so small as not to be shown in FIG. 7F has occurred,and the requirement for the quick response characteristic of control issatisfied while overshoot is moderately suppressed. That is, an idealresponse waveform is obtained. As compared with the cases shown in FIGS.7B and 7D, the evaluation function value G in the case shown in FIG. 7Fis the smallest value near 0. Therefore, the trajectory of thecontrolled variable PV in FIG. 7F is registered as an ideal controlresponse characteristic in the ideal control result storage unit 26.

As is also obvious from FIG. 7D, the overshoot amount OS_sim changeswith the lapse of the time T during the first simulation. The value ofthe overshoot amount OS_sim is finally used when its absolute value ismaximized. Although the first evaluation function value G calculated instep 204 also changes with the lapse of the time T, the value of theevaluation function value G is finally used when the absolute value ofthe overshoot amount OS_sim is maximized. In order to obtain the finalvalue of the evaluation function value G, it suffices to use thecalculated value in step 204 as the new evaluation function value G onlywhen the calculated value exceeds the previously used value of theevaluation function value G and discard the calculated value when it isequal to or less than the previously used evaluation function value G.

The PID parameter search computation unit 28 then performs the PIDparameter creation processing described in the first embodiment (step209), and the second simulation computation unit 25 performs the secondinitialization processing (step 210). The second initializationprocessing is the same as the initialization processing described in thefirst embodiment.

Subsequently, the second simulation computation unit 25 performs thesecond simulation at elapsed time T=0 (step 211). The second simulationprocessing at elapsed time T=0 is the same as the simulation processingat elapsed time T=0 described in the first embodiment.

After the execution of the second simulation processing, the simulationcomputation unit 25 determines whether the elapsed time T has reachedTsim (step 213). If the elapsed time T has not reached Tsim, it isdetermined that the second simulation processing is not complete. Theflow then returns to step 211 to perform the second simulationprocessing at elapsed time T>0. The second simulation processing atelapsed time T>0 is the same as the simulation processing at elapsedtime T>0 described in the first embodiment. The second evaluationfunction computation unit 27 then executes the second evaluationfunction processing of calculating a second evaluation function value F(step 212). The second evaluation function processing is the same as theevaluation function processing described in the first embodiment.

If it is determined in step 213 that T≧Tsim holds, the second simulationcomputation unit 25 determines that the second simulation processing iscomplete, and determines whether the processing in steps 209 to 213 iscomplete for all the combinations of the PID parameters (step 214). Ifthe processing in steps 209 to 213 is complete for all the combinationsof the PID parameters, the flow advances to step 215. If the processingis not complete, the flow returns to step 209 to cause the PID parametersearch computation unit 28 to create a new combination of theproportional band Pb, integral time Ti, and derivative time Td.

If the processing in steps 209 to 213 is complete for all thecombinations of the PID parameters, the PID parameter search computationunit 28 executes the second evaluation function value comparisonprocessing to extract a combination of the proportional band Pb,integral time Ti, and derivative time Td which provides a minimumevaluation function value F_min (step 215), and uses the extractedcombination of the proportional band Pb, integral time Ti, andderivative time Td as a parameter adjustment result (step 216).

With the above operation, the processing by the PID parameter adjustmentdevice is terminated.

According to this embodiment, the first simulation is performed tosimulate the transition state of a controlled system by applying themanipulated variable upper limit value MVH or manipulated variable lowerlimit value MVL defined as a response condition variable to thecontrolled system for the manipulated variable maintenance time dTmv,and the difference between the first simulation result and the idealresponse result is provided as the first evaluation function value G. Anideal control response characteristic is obtained by repeating the firstsimulation so as to make the first evaluation function value G approachan optimal value. This ideal control response characteristic is thenregistered in the ideal control result storage unit 26, thus searchingfor the PID parameters described in the first embodiment. This makes itpossible to realize optimal PID parameter adjustment without requiringthe user to have expertise about control.

Note that this embodiment is based on the assumption that OS does notbecome 0 or less, and a response time may be added to the firstevaluation function. In evaluation with OS_sim=OS alone, even in thecase shown in FIG. 7B in which the controlled variable PV slowlyapproaches the set point SP2, the first evaluation function value G maybecome an optimal value. As a consequence, many first simulation resultswhich provide the optimal evaluation function value G appear, and henceideal control response characteristics may not be narrowed down to onecharacteristic. Therefore, by adding evaluation based on whether theresponse time is minimum to the first evaluation function, a trulyoptimal result can be extracted from the first simulation results whichprovide the optimal evaluation function value G, thereby narrowing downideal control response characteristics to one. This can prevent wrongoptimization, i.e., using, as an ideal control response characteristic,a case wherein the controlled variable PV slowly approaches the setpoint SP2.

Third Embodiment

The third embodiment of the present invention will be described next.FIG. 8 is a block diagram showing the arrangement of a PID parameteradjustment device according to the third embodiment of the presentinvention. FIG. 9 is a flowchart showing the operation of the PIDparameter adjustment device in FIG. 8.

The PID parameter adjustment device of this embodiment is obtained byadding, to the PID parameter adjustment device of the first embodiment,an auto-tuning computation unit 9 which executes auto-tuning simulationprocessing of calculating an estimated value of a PID parameter from theresponse from a controlled system represented by a mathematical model ina model storage unit 1 by applying a manipulated variable with apredetermined amplitude to the controlled system, and a PID parametersearch range setting unit 10 which determines a PID parameter searchrange on the basis of the estimated value of the PID parameter and setsthe range in a PID parameter search computation unit 8.

The PID parameter adjustment device starts the processing in FIG. 9 inaccordance with, for example, a request from the user. First of all, theauto-tuning computation unit 9 executes auto-tuning simulationprocessing comprising manipulated variable output processing, deviationextreme value detection processing, switching elapsed time detectionprocessing, and PID parameter estimated value calculation processing.

In the manipulated variable output processing in step 301 in FIG. 9, theauto-tuning computation unit 9 executes the processing represented byexpression (13) if Kp>0, and executes the processing represented byexpression (14) if Kp<0:if SP−PV>0 then MV=MVH else MV=MVL  (13)if SP−PV>0 then MV=MVL else MV=MVH  (14)

Expression (13) indicates that if a set point SP is larger than acontrolled variable PV, a manipulated variable MV is set to amanipulated variable upper limit value MVH, and if the set point SP isequal to or less than the controlled variable PV, the manipulatedvariable MV is set to a manipulated variable lower limit value MVL.Expression (14) indicates that if the set point SP is larger than thecontrolled variable PV, the manipulated variable MV is set to themanipulated variable lower limit value MVL, and if the set point SP isequal to or less than the controlled variable PV, the manipulatedvariable MV is set to the manipulated variable upper limit value MVH.

FIG. 10 is a flowchart showing the details of deviation extreme valuedetection processing and switching elapsed time detection processing(step 302) by the auto-tuning computation unit 9. FIG. 11 is a view forexplaining the deviation extreme value detection processing andswitching elapsed time detection processing.

First of all, the auto-tuning computation unit 9 computes the controlledvariable PV according to equation (8). The auto-tuning computation unit9 then sets SP-PV to a deviation Er (step 501 in FIG. 10), anddetermines whether the following equality holds (step 502):|Er|>|Ermax|  (15)where Ermax is the maximum value of a deviation. The initial value ofthe deviation is 0. The auto-tuning computation unit 9 sets Ermax=Ex,i.e., sets the current deviation Er to a maximum deviation Ermax, wheninequality (15) holds (step 503).

The auto-tuning computation unit 9 determines according to the followinginequality whether the polarity of the deviation Er has been switched(step 504):ErEr0<0  (16)where Er0 is a deviation before one period. If inequality (16) does nothold, it is determined that the deviation extreme value detection is notcomplete, and the flow returns to step 301.

When the processing in steps 301 and 302 (steps 501 to 504) in FIG. 9 isrepeated in every cycle, the maximum deviation Ermax is updated as thedeviation Er increases. At time t1 in FIG. 11, inequality (16) holds.

When inequality (16) holds, the auto-tuning computation unit 9 setsEr1=Ermax, i.e., sets the maximum deviation Ermax to a first extremedeviation Er1. The auto-tuning computation unit 9 sets the time betweenthe time when inequality (16) holds and the latest time when the maximumdeviation Ermax is updated to a first manipulated variable switchingelapsed time Th1 (step 505). Note that if inequality (16) holds for thefirst time, the first manipulated variable switching elapsed time Th1 isset to 0.

The auto-tuning computation unit 9 then determines whether the deviationextreme value detection completion condition holds (step 506). In thisembodiment, the deviation extreme value detection completion conditionis that four extreme values of the controlled variable PV are detected.In this case, since only one extreme value of the controlled variable PVis detected, it is determined that the deviation extreme value detectionis not complete. The maximum deviation Ermax is initialized to 0 (step507), and the flow returns to step 301.

The processing in steps 301 and 302 (steps 501 to 504) is repeated inevery cycle, and inequality (16) holds again at time t3 in FIG. 11. Wheninequality (16) holds, the auto-tuning computation unit 9 sets Er2=Er1,Er1=Ermax, and Th2=Th1, i.e., substitutes the value of the first extremedeviation Er1 into the second extreme deviation Er2, sets the maximumdeviation Ermax to the first extreme deviation Er1, and substitutes thevalue of the first manipulated variable switching elapsed time Th1 intothe second manipulated variable switching elapsed time Th2. In addition,the auto-tuning computation unit 9 sets the time from time t1 wheninequality (16) holds to latest time t2 when the maximum deviation Ermaxis updated to the new first manipulated variable switching elapsed timeTh1 (step 505).

The auto-tuning computation unit 9 determines whether the deviationextreme value detection completion condition holds (step 506). In thiscase, since only two extreme values of the controlled variable PV aredetected, it is determined that the deviation extreme value detection isnot complete, and the maximum deviation Ermax is initialized to 0 (step507). The flow then returns to step 301.

The processing in steps 301 and 302 is repeated in every cycle, andinequality (16) holds again at time t5 in FIG. 11. When inequality (16)holds, the auto-tuning computation unit 9 sets Er3=Er2, Er2=Er1,Er1=Ermax, and Th2=Th1, i.e., substitutes the value of the secondextreme deviation Er2 into the third extreme deviation Er3, substitutesthe value of the first extreme deviation Er1 into the second extremedeviation Er2, sets the maximum deviation Ermax to the first extremedeviation Er1, and substitutes the value of the first manipulatedvariable switching elapsed time Th1 into the second manipulated variableswitching elapsed time Th2. In addition, the auto-tuning computationunit 9 sets the time from time t3 when inequality (16) holds to latesttime t4 when the maximum deviation Ermax is updated to the new firstmanipulated variable switching elapsed time Th1 (step 505).

The auto-tuning computation unit 9 determines whether the deviationextreme value detection completion condition holds (step 506). In thiscase, since only three extreme values of the controlled variable PV aredetected, it is determined that the deviation extreme value detection isnot complete, and the maximum deviation Ermax is initialized to 0 (step507). The flow then returns to step 301.

The processing in steps 301 and 302 is repeated in every cycle, andinequality (16) holds again at time t7 in FIG. 11. When inequality (16)holds, the auto-tuning computation unit 9 sets Er3=Er2, Er2=Er1,Er1=Ermax, and Th2=Th1, and sets the time from time t5 when inequality(16) holds to latest time t6 when the maximum deviation Ermax is updatedto the new first manipulated variable switching elapsed time Th1 (step505).

The auto-tuning computation unit 9 determines whether the deviationextreme value detection completion condition holds (step 506). In thiscase, since four extreme values of the controlled variable PV aredetected, it is determined that the deviation extreme value detection iscomplete, and the maximum deviation Ermax is initialized to 0 (step507). The flow then advances to step 303.

When manipulated variable output processing, deviation extreme valuedetection processing, and switching elapsed time detection processingare repeatedly executed in predetermined cycles in the above manner, andthe deviation extreme value detection completion condition holds, theauto-tuning simulation is terminated. As is also obvious from FIG. 11,although the number of extreme values of the controlled variable PVwhich are required for the calculation of PID parameters is essentiallythree, since the first extreme value may be inappropriate for thecalculation of parameters, four extreme values of the controlledvariable PV are detected.

After the termination of the auto-tuning simulation, the auto-tuningcomputation unit 9 calculates an estimated value of PID parameters,i.e., an estimated value Pbx of a proportional band, an estimated valueTix of an integral time, and an estimated value Tdx of a derivative timeaccording to the following equations (step 303):Pbx=100|Er1−Er2|/(0.9|MVH−MVL|)  (17)Tix=Th1+Th2  (18)T=0.21(Th1+Th2)  (19)

The PID parameter search range setting unit 10 sets a search range forPID parameters for the PID parameter search computation unit 8 asfollows on the basis of the calculated estimated value Pbx of theproportional band, the calculated estimated value Tix of the integraltime, and the calculated estimated value Tdx of the derivative time(step 304):0.5Pbx<Pb<2Pbx  (20)0.5Tix<Ti<2Tix  (21)0<Td<4Tdx  (22)

The settings for the PID parameter search computation unit 8 indicatethat the range of 0<Pb<Pb_max which the above proportional band Pb cantake is re-set to inequality (20), the range of 0<Ti<Ti_max which theintegral time Ti can take is re-set to inequality (21), and the range of0<Td<Td_max which the derivative time Td can take is re-set toinequality (22).

After the PID parameter search ranges are set, the model storage unit 1,a PID controller storage unit 2, a constraint condition storage unit 3,a simulation specification storage unit 4, a simulation computation unit5, an evaluation function computation unit 7, and the PID parametersearch computation unit 8 execute the processing described in the firstembodiment. The processing in steps 305 to 312 in FIG. 9 is the same asthat in steps 101 to 108 in FIG. 2.

In this embodiment, the limit cycle auto-tuning method of generatinglimit cycles with a predetermined manipulated variable width andadjusting PID parameters is executed by a simulation to estimate PIDparameter adjustment results, and the processing in the first embodimentis executed upon narrowing down search ranges to around the estimatedPID parameter values. This makes it possible to shorten the timerequired to search for optimal PID parameters in this embodiment ascompared with the first embodiment.

In this embodiment, the auto-tuning computation unit 9 and the PIDparameter search range setting unit 10 are added to the PID parameteradjustment device of the first embodiment. However, these units may beadded to the PID parameter adjustment device of the second embodiment toset PID parameter search ranges for the PID parameter search computationunit 28.

In addition, each of the PID parameter adjustment devices described inthe first to third embodiments can be implemented by a computercomprising an arithmetic unit, storage device, and interface andprograms for controlling these hardware resources.

INDUSTRIAL APPLICABILITY

The present invention can be applied to parameter adjustment for acontroller for PID control and the like.

1. A parameter adjustment device which adjusts a control parameter for acontroller which calculates a manipulated variable by performingcomputation based on the control parameter, characterized by comprising:a model storage unit which stores a mathematical model of a controlledsystem in advance; a controller storage unit which stores, in advance, acontroller algorithm by which the controller controls the controlledsystem; a constraint condition storage unit which stores a constraintcondition on operation of the controller in advance; a simulationcomputation unit which performs a simulation on the basis of theconstraint condition to simulate a control response of a control systemincluding the controlled system represented by the mathematical modeland a controller represented by the controller algorithm; an idealcontrol result storage unit which stores an ideal control responsecharacteristic of the control system in advance; an evaluation functioncomputation unit which computes an evaluation function valuerepresenting a proximity between a result of the simulation and theideal control response characteristic; and a parameter searchcomputation unit which causes said simulation computation unit toexecute the simulation while sequentially changing the control parameterfor the controller algorithm, and uses, as a parameter adjustmentresult, a control parameter which makes the evaluation function valuebecome an optimal value.
 2. A parameter adjustment device which adjustsa control parameter for a controller which calculates a manipulatedvariable by performing computation based on the control parameter,characterized by comprising: a model storage unit which stores amathematical model of a controlled system in advance; a controllerstorage unit which stores, in advance, a controller algorithm by whichthe controller controls the controlled system; a constraint conditionstorage unit which stores a constraint condition on operation of thecontroller in advance; an ideal control result storage unit which storesan ideal control response characteristic associated with a controlsystem including a controlled system represented by the mathematicalmodel and a controller represented by the controller algorithm; a firstsimulation computation unit which performs a first simulation tosimulate a transition state of the controlled system by applying anupper limit manipulated variable or a lower limit manipulated variabledefined by the constraint condition to the controlled system for amanipulated variable maintenance time; an ideal response result storageunit which stores an ideal response result as an ideal result of thefirst simulation in advance; a first evaluation function computationunit which computes a first evaluation function value representing aproximity between a result of the first simulation and the idealresponse result; a manipulated variable maintenance time searchcomputation unit which extracts a manipulated variable maintenance timewhich makes the first evaluation function value become an optimal valueby causing said first simulation computation unit to execute the firstsimulation while sequentially changing the manipulated variablemaintenance time; an ideal response waveform registration processingunit which registers a result of the first simulation corresponding tothe extracted manipulated variable maintenance time as the ideal controlresponse characteristic in said ideal control result storage unit; asecond simulation computation unit which performs a second simulation tosimulate a control response of the control system on the basis of theconstraint condition; a second evaluation function computation unitwhich computes a second evaluation function value representing aproximity between a result of the second simulation and the idealcontrol response characteristic registered in said ideal control resultstorage unit; and a parameter search computation unit which causes saidsecond simulation computation unit to execute the second simulationwhile sequentially changing the control parameter for the controlleralgorithm, and uses, as a parameter adjustment result, a controlparameter which makes the second evaluation function value become anoptimal value.
 3. A parameter adjustment device according to claim 1,characterized by further comprising an auto-tuning computation unitwhich executes auto-tuning simulation processing of calculating anestimated value of the control parameter from a response of thecontrolled system by applying a manipulated variable with apredetermined amplitude to the controlled system, and a parameter searchrange setting unit which determines a search range of the controlparameter on the basis of the estimated value of the control parameterand sets the search range in said parameter search computation unit. 4.A parameter adjustment device according to claim 2, characterized byfurther comprising an auto-tuning computation unit which executesauto-tuning simulation processing of calculating an estimated value ofthe control parameter from a response of the controlled system byapplying a manipulated variable with a predetermined amplitude to thecontrolled system, and a parameter search range setting unit whichdetermines a search range of the control parameter on the basis of theestimated value of the control parameter and sets the search range insaid parameter search computation unit.